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The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling.

Uragun B, Rajan R - BMC Neurosci (2013)

Bottom Line: This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data.These seven ILD function classes were found to map to the four "known" ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes.This indicates that these seven templates can be utilized in future modelling studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physiology Department, Monash University, Clayton, Victoria 3800, Australia. uragun@hotmail.com.

ABSTRACT

Background: A major cue for the position of a high-frequency sound source in azimuth is the difference in sound pressure levels in the two ears, Interaural Level Differences (ILDs), as a sound is presented from different positions around the head. This study aims to use data classification techniques to build a descriptive model of electro-physiologically determined neuronal sensitivity functions for ILDs. The ILDs were recorded from neurons in the central nucleus of the Inferior Colliculus (ICc), an obligatory midbrain auditory relay nucleus. The majority of ICc neurons (~ 85%) show sensitivity to ILDs but with a variety of different forms that are often difficult to unambiguously separate into different information-bearing types. Thus, this division is often based on laboratory-specific and relatively subjective criteria. Given the subjectivity and non-uniformity of ILD classification methods in use, we examined if objective data classification techniques for this purpose. Our key objectives were to determine if we could find an analytical method (A) to validate the presence of four typical ILD sensitivity functions as is commonly assumed in the field, and (B) whether this method produced classifications that mapped on to the physiologically observed results.

Methods: The three-step data classification procedure forms the basic methodology of this manuscript. In this three-step procedure, several data normalization techniques were first tested to select a suitable normalization technique to our data. This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data. Finally Cluster Analysis technique was applied to determine the number of clustered data with the aid of the CCC and Inconsistency Coefficient values.

Results: The outcome of a three-step analytical data classification process was the identification of seven distinctive forms of ILD functions. These seven ILD function classes were found to map to the four "known" ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes. This indicates that these seven templates can be utilized in future modelling studies.

Conclusions: We developed a taxonomy of ILD sensitivity functions using a methodological data classification approach. The number and types of generic ILD function patterns found with this method mapped well on to our electrophysiologically determined ILD sensitivity functions. While a larger data set of the latter functions may bring a more robust outcome, this good mapping is encouraging in providing a principled method for classifying such data sets, and could be well extended to other such neuronal sensitivity functions, such as contrast tuning in vision.

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The selection of inconsistency coefficient. The Inconsistency Coefficient varies by the number of clusters (as a depth value in Dendrogram) for number of linkage distributions. This distribution becomes in more compact form around the maximum value of Inconsistency Coefficient of seven. This value suggests the cut-off point for the Dendrogram or the number of cluster selection.
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Figure 5: The selection of inconsistency coefficient. The Inconsistency Coefficient varies by the number of clusters (as a depth value in Dendrogram) for number of linkage distributions. This distribution becomes in more compact form around the maximum value of Inconsistency Coefficient of seven. This value suggests the cut-off point for the Dendrogram or the number of cluster selection.

Mentions: Using these two algorithms for the Cluster Analysis led us to investigate the Inconsistency Coefficient, FigureĀ 5. From the Inconsistency Coefficient, we found out the natural segregation between the clusters is realized by a certain depth value.


The discrimination of interaural level difference sensitivity functions: development of a taxonomic data template for modelling.

Uragun B, Rajan R - BMC Neurosci (2013)

The selection of inconsistency coefficient. The Inconsistency Coefficient varies by the number of clusters (as a depth value in Dendrogram) for number of linkage distributions. This distribution becomes in more compact form around the maximum value of Inconsistency Coefficient of seven. This value suggests the cut-off point for the Dendrogram or the number of cluster selection.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126173&req=5

Figure 5: The selection of inconsistency coefficient. The Inconsistency Coefficient varies by the number of clusters (as a depth value in Dendrogram) for number of linkage distributions. This distribution becomes in more compact form around the maximum value of Inconsistency Coefficient of seven. This value suggests the cut-off point for the Dendrogram or the number of cluster selection.
Mentions: Using these two algorithms for the Cluster Analysis led us to investigate the Inconsistency Coefficient, FigureĀ 5. From the Inconsistency Coefficient, we found out the natural segregation between the clusters is realized by a certain depth value.

Bottom Line: This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data.These seven ILD function classes were found to map to the four "known" ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes.This indicates that these seven templates can be utilized in future modelling studies.

View Article: PubMed Central - HTML - PubMed

Affiliation: Physiology Department, Monash University, Clayton, Victoria 3800, Australia. uragun@hotmail.com.

ABSTRACT

Background: A major cue for the position of a high-frequency sound source in azimuth is the difference in sound pressure levels in the two ears, Interaural Level Differences (ILDs), as a sound is presented from different positions around the head. This study aims to use data classification techniques to build a descriptive model of electro-physiologically determined neuronal sensitivity functions for ILDs. The ILDs were recorded from neurons in the central nucleus of the Inferior Colliculus (ICc), an obligatory midbrain auditory relay nucleus. The majority of ICc neurons (~ 85%) show sensitivity to ILDs but with a variety of different forms that are often difficult to unambiguously separate into different information-bearing types. Thus, this division is often based on laboratory-specific and relatively subjective criteria. Given the subjectivity and non-uniformity of ILD classification methods in use, we examined if objective data classification techniques for this purpose. Our key objectives were to determine if we could find an analytical method (A) to validate the presence of four typical ILD sensitivity functions as is commonly assumed in the field, and (B) whether this method produced classifications that mapped on to the physiologically observed results.

Methods: The three-step data classification procedure forms the basic methodology of this manuscript. In this three-step procedure, several data normalization techniques were first tested to select a suitable normalization technique to our data. This was then followed by PCA to reduce data dimensionality without losing the core characteristics of the data. Finally Cluster Analysis technique was applied to determine the number of clustered data with the aid of the CCC and Inconsistency Coefficient values.

Results: The outcome of a three-step analytical data classification process was the identification of seven distinctive forms of ILD functions. These seven ILD function classes were found to map to the four "known" ideal ILD sensitivity function types, namely: Sigmoidal-EI, Sigmoidal-IE, Peaked, and Insensitive, ILD functions, and variations within these classes. This indicates that these seven templates can be utilized in future modelling studies.

Conclusions: We developed a taxonomy of ILD sensitivity functions using a methodological data classification approach. The number and types of generic ILD function patterns found with this method mapped well on to our electrophysiologically determined ILD sensitivity functions. While a larger data set of the latter functions may bring a more robust outcome, this good mapping is encouraging in providing a principled method for classifying such data sets, and could be well extended to other such neuronal sensitivity functions, such as contrast tuning in vision.

Show MeSH
Related in: MedlinePlus